System for extracting text from images

ABSTRACT

A system for extracting text from images comprises a processor configured to receive a digital copy of an image and identify a portion of the image, wherein the portion comprises text to be extracted. The processor further determines orientation of the portion of the image, and extracts text from the portion of the image considering the orientation of the portion of the image.

BACKGROUND

Unless otherwise indicated herein, the materials described in thissection are not prior art to the claims in this application and are notadmitted to being prior art by inclusion in this section.

Field of the Invention

The subject matter in general relates to system for data extraction.More particularly, but not exclusively, the subject matter relates toextracting text from images.

Discussion of the Related Art

Computerized document processing includes scanning of logos, stamps ordocuments and the conversion of an actual image of the logos, stamps ordocuments into an electronic image of the logos, stamps or documents.The electronic image of the document may comprise text, wherein the textmay be of varying sizes, orientations, font and so on. The text may beextracted from the images for obtaining data or information, which maybe of interest. The data may include date, address and so on. The textmay be extracted from the image of the document by optical characterrecognition (OCR) techniques.

In certain cases, the images of the scanned documents may not be of afixed orientation with respect to a reference axis. That is to say, eachof the image to be processed may be oriented differently. In such cases,the OCR techniques may fail, as the OCR techniques may not be able toprocess the random orientation of the images. In such scenarios,re-orientation may have to done manually. Manual re-orientation of thetext or images may be time consuming when the data to be processed issignificantly large.

Additionally, the OCR techniques may fail to produce an accurate resultwhen the text to be extracted from the image may not be orientedhorizontally. That is to say, the different characters and text inlogos, stamps or scanned documents may possess different orientationwith respect to a reference axis.

Further, most of the current OCR techniques aim to extract text fromimages with a simple background. When the relevant text to be extractedis occluded by a complex background (text or image background), the OCRtechniques may not be able to completely isolate the complex backgroundfrom the relevant data. As an example, the OCR techniques may produceaccurate results when the background of the relevant data is plane. Buton the other hand, OCR techniques may fail to produce an optimum result,when the background text overlap the relevant text.

As an example, a page in a passport may include a stamp, placed by animmigration officer. The stamp may include a data, which may be the dataor information that may be of interest. The stamp may be placed overother text that may be in the background. Furthermore, stamp as well asthe text therein may be in an orientation different from that of thetext in the background. Machine driven extraction of data of interest(e.g., date) in such a scenario poses a significant challenge.

In view of the foregoing discussions, there is a need for an improvedtechnique for extracting data from composite images.

SUMMARY

In one aspect, a system is provided for extracting text from images. Thesystem comprises a processor configured to receive a digital copy of animage and identify a portion of the image, wherein the portion comprisestext to be extracted. The processor further determines orientation ofthe portion of the image, and extracts text from the portion of theimage considering the orientation of the portion of the image.

In another aspect, a method is provided for extracting text from images.The method comprises receiving, by a computing infrastructure, a digitalcopy of an image and identifying a portion of the image, wherein theportion comprises text to be extracted. The method further comprisesdetermining, by the computing infrastructure, orientation of the portionof the image, and extracting text from the portion of the imageconsidering the orientation of the portion of the image.

BRIEF DESCRIPTION OF DRAWINGS

This disclosure is illustrated by way of example and not limitation inthe accompanying figures. Elements illustrated in the figures are notnecessarily drawn to scale, in which like references indicate similarelements and in which:

FIG. 1A illustrates a system 100 for extracting text from images, inaccordance with an embodiment;

FIG. 1B illustrates various modules of the system 100 for extractingtext from the images, in accordance with an embodiment;

FIG. 2A illustrates relevant portion 202 within a relevant section 212of an image 206, in accordance with an embodiment;

FIG. 2B illustrates a correct orientation of the relevant portion 202,in accordance with an embodiment;

FIG. 3 illustrates character bounding boxes 302, in accordance with anembodiment;

FIG. 4A is a flowchart 400 illustrating the steps involved in trainingof a first custom deep neural network, in accordance with an embodiment;

FIG. 4B illustrates a labelled training image 412 for training of thefirst custom deep neural network for identifying the relevant portion202 within the image 412, in accordance with an embodiment;

FIG. 5 is a flowchart 500 illustrating the steps involved in training ofa second custom deep neural network for determining the orientation ofthe relevant portion 202 within the image 206, in accordance with anembodiment;

FIG. 6 is a flowchart 600 illustrating the steps involved in training ofa third custom deep neural network for detecting characters within thetext 204, in accordance with an embodiment;

FIG. 7 is a flowchart 700 illustrating the training of a fourth customdeep neural network for classifying the characters within the text 204,in accordance with an embodiment;

FIG. 8A is a flowchart 800A, illustrating the steps involved in thedetermination of the orientation of the relevant portion 202 inreal-time, in accordance with an embodiment;

FIG. 8B is a flowchart 800B illustrating the steps involved inextracting the relevant text 204 from the relevant portion 202 inreal-time, in accordance with an embodiment; and

FIG. 9 is a block diagram illustrating hardware elements of the system100 of FIG. 1A, in accordance with an embodiment.

DETAILED DESCRIPTION OF THE INVENTION

The following detailed description includes references to theaccompanying drawings, which form part of the detailed description. Thedrawings show illustrations in accordance with example embodiments.These example embodiments are described in enough detail to enable thoseskilled in the art to practice the present subject matter. However, itmay be apparent to one with ordinary skill in the art that the presentinvention may be practised without these specific details. In otherinstances, well-known methods, procedures and components have not beendescribed in detail so as not to unnecessarily obscure aspects of theembodiments. The embodiments can be combined, other embodiments can beutilized, or structural and logical changes can be made withoutdeparting from the scope of the invention. The following detaileddescription is, therefore, not to be taken in a limiting sense.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one. In this document, the term“or” is used to refer to a non-exclusive “or”, such that “A or B”includes “A but not B”, “B but not A”, and “A and B”, unless otherwiseindicated.

It should be understood that the capabilities of the invention describedin the present disclosure and elements shown in the figures may beimplemented in various forms of hardware, firmware, software, recordablemedium or combinations thereof.

FIG. 1A illustrates a system 100 (e.g., computing infrastructure) forextracting data 204 from composite images 206 (refer FIG. 2A), inaccordance with an embodiment. The system 100 may communicate with I/Odevice 10 via a communication network 12. Alternatively, the I/O device10 may be part of the system 100. The system 100, which may be a server,may receive images from the I/O device 10 via the communication network12.

Referring to the figures, and more particularly to FIG. 1B and FIG. 2A,the system 100 for extracting relevant text 204 (data) from the image206 is provided, in accordance with an embodiment. The image 206 maycomprise a relevant section 212, wherein the relevant section 212 mayinclude a relevant portion 202 (second image). As an example, the image206 may be a page from a passport and the relevant portion 202 may be astamp placed within the image 206, wherein the relevant section 212 maybind the stamp 202. The system 100 may be trained for identifying andprocessing the relevant portion 202 of the image 206. The relevantportion 202 of the image 206 may correspond to stamps, logos and so on.That is to say, the system 100 may be trained to identify stamps, logosand so on, from the images 206 and then process the extracted stamps,logos and so on, to extract a relevant text 204 present within thestamps, logos and so on.

In an embodiment, referring to FIG. 1B, for extracting text 204 from theimage 206, the system 100 may comprise an extracting module 102, anorientation detection module 104, an object detection module 106, aclassification module 108, a sorting module 110 and a formatting module112. The instructions of each of these modules may be executed by one ormore processors 902 (refer FIG. 9). The instructions may be stored inthe memory unit 904 (refer FIG. 9).

In an embodiment, referring to FIG. 2A and FIG. 3, the text 204 may beextracted from images 206, wherein the image 206 may be a compositeimage. That is to say, the text 204 within the relevant portion 202 ofthe image 206 may be occluded by background text 216 (‘irrelevantcharacters 216’). Further, the relevant portion 202 of the image 206 maynot be horizontally oriented with respect to a reference. As an example,the stamp 202 of the image 206 may be oriented differently with respectto the reference. Further, the date 204 marked on the stamp 202 may beoccluded by the background text 216.

In an embodiment, referring to FIG. 2A, the extracting module 102 may beconfigured to identify and extract the relevant section 212 of the image206. The relevant section 212 may include the relevant portion 202 (e.g.stamp, logo or the like), and the image 206 may correspond to passport,letters or the like. In this example, the relevant portion 202corresponds to a stamp 202. The extracting module 102 may comprise afirst custom deep neural network, wherein the first custom deep neuralnetwork may be trained to identify (detect) the relevant portion 202 ofthe image 206. The extracting module 102 may further comprise a croppingmodule. The cropping module may be configured to extract the relevantsection 212 comprising the relevant portion 202 within the image 206. Asan example, the extracting module 102 may first identify the stamp 202within the image 206, using the first custom deep neural network andthen, may crop out the relevant section 212 that binds the stamp 202from the image 206 using the cropping module.

In an embodiment, referring to FIG. 2A, the orientation detection module104 may determine the orientation of the relevant portion 202. Theorientation may be determined with respect to reference. The referencemay be within the relevant portion 202. In certain scenarios, therelevant portion 202 of image 206 may not be horizontally oriented withrespect to the reference, wherein the reference may be a text 204 withinthe relevant portion 202. As an example, the stamp 202 may be orientedat 315° (anticlockwise) or 45° (clockwise). In other words, the text 204(reference) may be oriented at 45° as compared to the image 206. Theorientation detection module 104 may be configured to comprise a secondcustom deep neural network to determine the orientation of the relevantportion 202 within the relevant section 212. The second custom deepneural network may classify orientation of the relevant portion 202 intoa group. There may be 360 groups as an example, wherein each of thegroups may correspond to each degree of angle. As an example, the firstgroup may correspond to 0°, the second group may correspond to 1°, thelast group may correspond to 359° and so on. The orientation detectionmodule 104 may further comprise a rotation correction module forcorrecting the orientation (reorientation) of the relevant portion 202,with respect to the reference. As an example, referring to FIG. 2B, thestamp 202 may be reoriented by an angle of 45° anticlockwise to correctthe orientation of the stamp 202.

In an embodiment, referring to FIG. 3, the object detection module 106may be configured to identify characters within the relevant text 204.The characters may be digits, alphabets and so on. The relevant portion202 may comprise of characters in which, some characters may be relevant(within the relevant text 204) and some may be irrelevant characters214, 216. The object detection module 106 may comprise a third customdeep neural network for detecting the characters within the relevanttext 204. The third custom deep neural network may be trained toidentify and bind each character of the relevant text 204 by differentboxes 302 (“character bounding boxes 302”). The output of the thirdcustom deep neural network may be coordinates of each character boundingbox 302. The object detection module 106 may further comprise thecropping module for cropping the character bounding boxes 302. That isto say, each of the characters of the relevant text 204 may be croppedout of the image 206. As an example, the characters ‘1’, ‘9’, ‘M’, ‘A’,‘Y’, ‘2’, ‘0’, ‘1’ and ‘9’, may be cropped out from the text 204. Theobject detection module 106 may not extract the characters in the samesequence as present in the relevant text 204. As an example, output ofthe object detection module 106 may be in an order: ‘M’, ‘Y’, ‘1’, ‘A’,‘9’, ‘9’, ‘1’, ‘2’ and ‘0’. The object detection module 106 may beconfigured to detect and crop out the relevant characters within therelevant text 204. The irrelevant characters 214, 216 within therelevant portion 202 may not be extracted by the object detection module106. In an embodiment, the irrelevant characters 214 may be oriented inthe same direction as that of the relevant characters within the text204. In an embodiment, the irrelevant characters 216 may be oriented inone or more directions that are different compared to the direction ofthe relevant characters within the text 204.

In an embodiment, the classification module 108 may be configured toclassify each of the characters into a character group. There may be 36groups, as an example, wherein each of the group may correspond to eachalphabet ‘A’ to ‘Z’ or digit ‘0’ to ‘9’. As an example, the firstcharacter group may correspond to alphabet ‘A’, the second charactergroup may correspond to ‘B’, the last group may correspond to ‘9’ and soon. The classification module 108 may comprise a fourth custom deepneural network for classifying the characters of the relevant text 204.The fourth custom deep neural network may be classification deep neuralnetwork. The classification module 108 may be trained for classifyingeach of the character into the character group. In an embodiment, theclassification module 108 may not provide the output in the samesequence as present in the relevant text 204. As an example, output ofthe classification module 108 may be in an order: ‘M’, ‘Y’, ‘1’, ‘A’,‘9’, ‘9’, ‘1’, ‘2’ and ‘0’.

In an embodiment, the sorting module 110 may be configured to sort thecharacters of the relevant text 204. The characters may be sorted usingthe coordinates of the bounding boxes 302 to obtain the same sequence aspresent in the text 204.

In an embodiment, the formatting module 112 may rearrange the charactersin a pre-set format. As an example, the pre-set format may beYYYY-MM-DD, wherein ‘YYYY’ represents the year, ‘MM’ represents themonth and ‘DD’ represents the date.

Having discussed about the various modules involved in extracting text204 from images 206, training of the different neural networks (firstcustom deep neural network, second custom deep neural network, thirdcustom deep neural network and fourth custom deep neural network) of thesystem 100 is discussed hereunder.

FIG. 4A is a flowchart 400 illustrating the training of the first customdeep neural network. The first custom deep neural network may be trainedfor identifying the relevant portion 202 within the image 206. As anexample, the first custom deep neural network may be trained to identifythe stamp 202 within the image 206 (refer FIG. 2A).

In an embodiment, at step 402, referring to FIG. 4B as well, the firstcustom deep neural network may receive plurality of labelled trainingimages 412. The plurality of labelled training images 412 may correspondto images of various documents comprising sample stamps 414 (“relevantsample portion 414”). The sample stamps 414 may be from variouscountries and of various orientations, fonts, formats, colours and soon. Coordinates of the sample stamp 414 within the training image 412may be determined manually and may be saved. The coordinates box/area416 (‘bounding box 416’) binding the sample stamps 414 may be saved, maycorrespond to a “true” coordinate value. That is to say, the firstcustom deep neural network may receive plurality of training images 412,wherein each of the training images 412 may be labelled with thecoordinates of areas binding the sample stamps 414.

In an embodiment, at step 404, the first custom deep neural network mayidentify an area defined by the co-ordinates of box 418 (predictedcoordinate value) as the area within which the relevant portion ispresent. However, the relevant sample portion 414 is within theco-ordinates defined by the bounding boxes 416 (true coordinate value).As explained earlier, each training image may be associated with itsrespective true coordinate values.

In an embodiment, at step 406, the first custom deep neural network maycompare the true coordinate value and the predicted coordinate value todetermine the occurrence of an error, if any. The error may be thedifference between the true coordinate value and the predictedcoordinate value. As is the case in the example of FIG. 4B, the firstcustom deep neural network may identify a different portion box 418 ascomprising the relevant sample portion. In such scenarios, the predictedcoordinate value may be different from the true coordinate value.

In an embodiment, at step 408, a loss is calculated using a first lossfunction that may be backpropagated through the first custom deep neuralnetwork to optimize parameters, wherein the parameters may be updated asguided by optimization techniques. Such errors will be minimized as theneural network gets trained.

In an embodiment, the trained first custom deep neural network may bepresent in the extracting module 102 of the system 100.

Having discussed about the training of the first custom deep neuralnetwork, training of the second custom deep neural network fordetermining the orientation of the relevant portion 202 is discussedhereunder.

FIG. 5 is a flowchart 500 illustrating the training of the second customdeep neural network. The second custom deep neural network may betrained for determining the orientation of the relevant portion 202within the relevant section 212.

In an embodiment, at step 502, a plurality of images with randomorientations with respect to the reference may be obtained fromdifferent sources. The plurality of images may form a training dataset,wherein the plurality of images may correspond to stamps, logos and thelike.

In an embodiment, at step 504, a slope of each image, with respect tothe reference, may be determined. The slope may correspond to theorientation of the image. The slope may be determined manually, usingconventional methods.

In an embodiment, at step 506, the images may be reoriented horizontallywith respect to the reference. That is to say, each image (whereverrequired) of the dataset may be reoriented such that the slope of eachof reoriented image may be 0° (refer FIG. 3).

In an embodiment, at step 508, each of the horizontally reorientedimages may be sampled to generate multiple images (e.g., orientationtraining images) with random orientations. That is to say, each of thehorizontally oriented image may be rotated by different angles and thenew sample images generated from the horizontal image may be saved. Therotation may be carried out by a software module, executed by aprocessor. Consequently, the dataset may comprise of a greater number ofimages compared to original number of images. As an example, theoriginal training dataset may comprise of 20,000 images of randomorientations. For training the second custom deep neural network,greater number of images may be required than the original 20,000 images(dataset). Therefore, to generate greater number of images the randomlyoriented images may be reoriented horizontally and each image may besampled to generate more images with various orientations. Consequently,the dataset may now comprise of more number of images than the original20,000 images. The orientation of each of the sample images may be savedand may correspond to a true value.

In an embodiment, at step 510, the labelled images may be fed to thesecond custom deep neural network, wherein the label corresponds to theorientation of the image. The second custom deep neural network may be adeep rotation custom neural network.

In an embodiment, at step 512, the second custom deep neural network maypredict the orientation of the images. The orientation of the images,predicated by the second custom deep neural network, may correspond to apredicted value. The predicted value of orientation of the images maynot be the same as the true value. As an example, the image may havebeen oriented to 45° (true value) whereas the orientation predicted bythe second custom deep neural network may be 43° (predicted value). Thatis to say, the predicted value (43°) is different from the true value(45°).

In an embodiment, at step 514, a difference between the true value andthe predicted value may be determined by the processor. The differencebetween the true value and the predicted value may correspond to anerror value. As an example, the difference between the true value (45°)and the predicted value (43°) may be 2°.

In an embodiment, at step 516, a second loss function may be determinedby the processor to minimize the error value to obtain the correctorientation value. The second loss function may backpropagate the errorvalue to hidden layers of the second custom deep neural network tominimize the error value.

In an embodiment, the trained second custom deep neural network may bepresent in the orientation detection module 104 of the system 100 forreal-time orientation detection.

Having discussed the training of the second custom deep neural networkfor orientation detection, training of the third custom deep neuralnetwork is discussed hereunder.

FIG. 6 is a flowchart 600 illustrating the training of the third customdeep neural network. The third custom deep neural network may be trainedfor detecting the characters within the relevant text 204 of the image206 (refer FIG. 2A). As an example, the third custom deep neural networkmay be trained to detect the characters within the date 204 markedwithin the stamp 202.

In an embodiment, at step 602, the third custom deep neural network mayreceive plurality of labelled training images. The plurality of imagesmay comprise dates in various formats. The various formats may benumeric format, wherein month, date of the month and year may berepresented as numerals, or alphanumeric format, wherein date of themonth and year may be represented as numerals and month may berepresented by letters. As an example, the format of the date may be03/05/2019 (numeric format) or 03 MAY 2019 (alphanumeric format).Coordinates of each character within the date may be determined manuallyand may be saved. That is to say, the third custom deep neural networkmay receive plurality of training images, wherein the training imagesmay be labelled with the coordinates of the characters within the date.The coordinates of the characters may correspond to a true output.

In an embodiment, at step 604, the third custom deep neural network mayidentify the characters and may bind each of the character within thedate using character bounding boxes. The coordinates of the characterbounding boxes may correspond to a predicted output.

In an embodiment, at step 606, the third custom deep neural network maycompare the predicted output and the true output to determine theoccurrence of an error.

In an embodiment, at step 608, the error is calculated using a thirdloss function that may be backpropagated to the hidden layers of thethird custom deep neural network to minimize the error. In this way, thethird custom deep neural network may be trained to identify and boundthe characters within the date, with occurrence of minimum error. Thesteps 604-608 may be executed by a processor.

In an embodiment, the trained third custom deep neural network may bepresent in the object detection module 106 of the system 100 forreal-time processing.

Having discussed about the training of the third custom deep neuralnetwork, training of the fourth custom deep neural network forclassifying the characters of the relevant text 204 is discussedhereunder.

FIG. 7 is a flowchart 700 illustrating the training of the fourth customdeep neural network for classifying the characters within the text 204.As an example, the fourth custom deep neural network may be trained toclassify the digits and alphabets within the date 204 marked on thestamps 202 into the character group.

In an embodiment, at step 702, the fourth custom deep neural network mayreceive plurality of labelled images. The images may correspond toimages of characters and the label may correspond to the correspondingcharacter group. The characters may be alphabets (‘A’ to ‘Z’) or digits(‘0’ to ‘9’). As an example, the image fed to the fourth custom deepneural network may be image of the character ‘A’ labelled as thecorresponding character group ‘A’. The label may correspond to a truecharacter label.

In an embodiment, at step 704, the fourth custom deep neural network mayclassify the character into one of the character group. The output ofthe fourth custom deep neural network may be a probability value of thecharacter being classified into the corresponding character group. Theprobability value may correspond to a predicted character label. Incertain scenarios, the true character label may not be the same as thepredicted character label. As an example, the image fed to the fourthcustom deep neural network may be labelled as ‘A’ (true character label)but the fourth custom deep neural network may generate the label ‘A’with only 98% probability value (predicted character label). That is tosay, the probability value of the image to be classified as ‘A’ is 98%,whereas the probability value should have been 100%.

In an embodiment, at step 706, the fourth custom deep neural network maycompare the true character label and the predicted character label todetermine an error. As an example, in the above mentioned example, theerror may be 0.2.

In an embodiment, at step 708, a fourth loss function may calculate theerror and may be backpropagated to hidden layers of the fourth customdeep neural network to optimize the output for classifying the imageinto the correct character group with minimum error. The steps 704-708may be executed by a processor.

In an embodiment, the trained fourth custom deep neural network may bepresent in the classification module 108 of the system 100 for real-timecharacter classification.

Having discussed about the training of the various neural networks,implementation of the system 100 is discussed hereunder.

FIG. 8A is a flowchart 800A illustrating the steps, executed by thesystem 100, involved in the determination of the orientation of therelevant portion 202 of the image 206 in real-time.

In an embodiment, at step 802, referring to FIG. 2A, the image 206 fromwhich the text 204 may be extracted, may be received. The image 206 maycorrespond to a scanned copy of documents such as passport, legaldocuments and so on. The image 206 may comprise of the relevant portion202 and the relevant text 204. As an example, the relevant portion 202may be the stamp and the relevant text 204 may be the date marked withinthe stamp.

In an embodiment, at step 804, the image 206 may be fed to theextracting module 102. The extracting module 102 may be configured tocomprise the trained first custom deep neural network and the croppingmodule. Referring to FIG. 2A, the trained first custom deep neuralnetwork may identify the relevant portion 202 within the image 206 andmay bind the relevant portion by the bounding box (‘relevant section212’). Then, the cropping module may crop out the bounding box 212comprising the stamp 202. The output of the extracting module 102 may bethe bounding box 212 comprising the stamp 202.

In an embodiment, at step 806, the relevant section 212 comprising thestamp 202 may be fed to the orientation detection module 104. Theorientation detection module 104 may be configured to comprise thetrained second custom deep neural network and the rotation correctionmodule.

In an embodiment, at step 806, the trained second custom deep neuralnetwork may determine the orientation of the relevant portion 202(‘stamp 202’) of the relevant section 212 with respect to the reference.The second custom deep neural network may further classify theorientation of the stamp 202 into one of the group, wherein each groupcorresponds to each degree of rotation. As an example, referring to FIG.2A, the second custom deep neural network may classify the orientationof the stamp 202 to the group 45°.

In an embodiment, at step 806, the orientation of the stamp 202 may becorrected using the rotation correction module. As an example, referringto FIG. 2B, the relevant section 212 comprising the stamp 202 may bereoriented by an angle of 45° anticlockwise to correct the orientationof the stamp 202.

In an embodiment, the reoriented image may be fed to a conventional OCR(Optical Character Recognition) device to extract the relevant text 204by conventional methods.

In another embodiment, referring to FIG. 8B, deep learning methods maybe used to extract the characters within the text 204 which is describedin detail below.

FIG. 8B is a flowchart 800B illustrating the steps, executed by thesystem 100, involved in extracting the relevant text 204 from therelevant portion 202.

In an embodiment, at step 812, the correctly oriented image 208 (referFIG. 2B) may be received from the the orientation detection module 104.The orientation may be corrected with respect to the reference withinthe stamp 202.

In an embodiment, at step 814, the correctly oriented image 208 (referFIG. 2B) may be fed to the object detection module 106. The objectdetection module 106 may be configured to comprise the trained thirdcustom deep neural network and the cropping module. The third customdeep neural network may detect the characters within the text 204 andmay bind them using the character bounding boxes 302 (refer FIG. 3),wherein each of the character bounding box 302 may comprise of eachcharacter of the relevant text 204. As an example, referring to FIG. 3,the third custom deep neural network may generate character boundingboxes 302 for each of the character (‘1’, ‘9’, ‘M’, ‘A’, ‘Y’, ‘2’, ‘0’,‘1’ and ‘9’) of the date 204 marked within the stamp 202. The output ofthe third custom deep neural network may be the coordinate of eachcharacter of the relevant text 204.

In an embodiment, at step 816, each of the bounding box 302 comprisingthe characters may be cropped out by the cropping module.

In an embodiment, at step 818, each of the cropped character (e.g. ‘M’,‘Y’, ‘0’, ‘A’, ‘9’, ‘2’, ‘1’, ‘1’ and ‘9’) may be fed to the trainedfourth custom deep neural network for classifying each of the characterto at least one of the 36 character groups.

In an embodiment, at step 820, each of the character may be sorted bytheir coordinates, by the sorting module 110. The characters may besorted to obtain the correct sequence. The correct sequence may be theoriginal sequence of the characters in the date 204. As an example, theoriginal sequence of the characters ‘1, 9, M, A, Y, 2, 0, 1, 9’ may becropped out by the object detection module 106 in the order ‘Y, A, 9, 1,M, 1, 9, 2, 0’. The sorting module 110 may sort the characters to thecorrect sequence ‘1, 9, M, A, Y, 2, 0, 1, 9’.

In an embodiment, at step 822, the sorted characters may be aligned in apre-set format by the formatting module 112. As an example, the pre-setformat may be YYYY-MM-DD, wherein ‘YYYY’ represents the year, ‘MM’represents the month and ‘DD’ represents the date. Consequently, thecharacters may be arranged as 2019-MAY-19.

FIG. 9 is a block diagram illustrating hardware elements of the system100, in accordance with an embodiment. The system 100 may be implementedusing one or more servers, which may be referred to as server 14. Thesystem 100 may include a processor 902, a memory unit 904, aninput/output module 906, and a communication interface 908. In anembodiment, the system 100 may be an electronic device and may includesmart phones, PDAs, tablet PCs, notebook PCs, laptops, computers orservers among other computing devices.

The processor 902 is implemented in the form of one or more processorsand may be implemented as appropriate in hardware, computer executableinstructions, firmware, or combinations thereof. Computer-executableinstruction or firmware implementations of the processor 902 may includecomputer-executable or machine-executable instructions written in anysuitable programming language to perform the various functionsdescribed.

The memory unit 904 may include a permanent memory such as hard diskdrive, may be configured to store data, and executable programinstructions that are implemented by the processors 902. The memory unit904 may be implemented in the form of a primary and a secondary memory.The memory unit 904 may store additional data and program instructionsthat are loadable and executable on the processor 902, as well as datagenerated during the execution of these programs. Further, the memoryunit 904 may be a volatile memory, such as a random access memory and/ora disk drive, or a non-volatile memory. The memory unit 904 may compriseof removable memory such as a Compact Flash card, Memory Stick, SmartMedia, Multimedia Card, Secure Digital memory, or any other memorystorage that exists currently or may exist in the future.

The input/output module 906 may provide an interface for input devices(e.g., I/O device 10) such as computing devices, scanner, touch screen,mouse, and stylus among other input devices; and output devices (e.g.,I/O device 10) such as printer, and additional displays among others.The input/output module 906 may be used to receive data or send datathrough the communication interface 908.

The communication interface 908 may include a modem, a network interfacecard (such as Ethernet card), a communication port, and a PersonalComputer Memory Card International Association (PCMCIA) slot, amongothers. The communication interface 908 may include devices supportingboth wired and wireless protocols. Data in the form of electronic,electromagnetic, optical, among other signals may be transferred via thecommunication interface 908.

The processes described above is described as a sequence of steps, thiswas done solely for the sake of illustration. Accordingly, it iscontemplated that some steps may be added, some steps may be omitted,the order of the steps may be re-arranged, or some steps may beperformed simultaneously.

The example embodiments described herein may be implemented in anoperating environment comprising software installed on a computer, inhardware, or in a combination of software and hardware.

Although embodiments have been described with reference to specificexample embodiments, it will be evident that various modifications andchanges may be made to these embodiments without departing from thebroader spirit and scope of the system and method described herein.Accordingly, the specification and drawings are to be regarded in anillustrative rather than a restrictive sense.

Many alterations and modifications of the present invention will nodoubt become apparent to a person of ordinary skill in the art afterhaving read the foregoing description. It is to be understood that thephraseology or terminology employed herein is for the purpose ofdescription and not of limitation. It is to be understood that thedescription above contains many specifications, these should not beconstrued as limiting the scope of the invention but as merely providingillustrations of some of the personally preferred embodiments of thisinvention.

What is claimed is,:
 1. A system for extracting text from images, thesystem comprising at least one processor configured to: receive adigital copy of an image; identify a portion of the image, wherein theportion comprises text to be extracted; determine orientation of theportion of the image; and extract text from the portion of the imageconsidering the orientation of the portion of the image.
 2. The systemas claimed in claim 1, wherein the portion comprises a second image,which is distinguishable within the image, wherein the second imagecomprises the text to be extracted.
 3. The system as claimed in claim 2,wherein the portion is identified by a first custom deep neural network,wherein the first custom deep neural network is trained by configuringthe system to: receive a plurality of training images withpre-identified portion of interest within each of the training images;predict portion of interest in each of the training images; and refinethe first custom deep neural network based on the pre-identified portionof interest and the predicted portion of interest corresponding to eachof the training images.
 4. The system as claimed in claim 2, wherein theorientation of the portion of the image is determined based on at leastone reference present in the second image.
 5. The system as claimed inclaim 2, wherein the orientation of the portion of the image isdetermined by a second custom deep neural network, wherein the secondcustom deep neural network is trained by configuring the system to:receive a plurality of orientation training images with pre-identifiedorientation corresponding to each of the orientation training images;predict orientation of each of the orientation training images; andrefine the second custom deep neural network based on the pre-identifiedorientation and the predicted orientation corresponding to each of theorientation training images.
 6. The system as claimed in claim 5,wherein the orientation training images comprises duplicate images,wherein the duplicate images are obtained by altering the orientation ofits original image.
 7. The system as claimed in claim 1, whereinextracting the text from the portion of the image is enabled byconfiguring the processor to: extract images, with each extracted imagecomprising individual characters; classify character present in each ofthe extracted images into character groups; and determine sequence inwhich each of the characters is presented in the portion based onco-ordinates of the extracted images within the portion.
 8. The systemas claimed in claim 7, wherein the extracted images of characters areamong characters present in the portion of the image, wherein at least aset of characters, which are within the characters present in theportion of the image, are not extracted.
 9. The system as claimed inclaim 8, wherein the set of characters comprises characters, which areoriented in the same direction as that of the characters in theextracted images, and characters, which are oriented in one or moredirections that are different compared to the direction of orientationof the characters in the extracted images.
 10. The system as claimed inclaim 7, wherein extraction of images comprising individual charactersis enabled by a third custom deep neural network, wherein the thirdcustom deep neural network is trained by configuring the system to:receive a plurality of labelled training images with pre-identifiedareas within each of the labelled training images, wherein each of theareas binds one character; predict areas, each comprising one character,in each of the labelled training images; and refine the third customdeep neural network based on the pre-identified areas and the predictedareas corresponding to each of the labelled training images.
 11. Thesystem as claimed in claim 7, wherein classifying character present ineach of the extracted images into character groups is enabled by afourth custom deep neural network, wherein the fourth custom deep neuralnetwork is trained by configuring the system to:z receive a plurality ofcharacter images corresponding to each of the character groups withpre-identified character for each of the character images; predictcharacter presented in each of the character images; and refine thefourth custom deep neural network based on the pre-identified characterand the predicted character corresponding to each of the characterimages.
 12. The system as claimed in claim 7, wherein the processor isfurther configured to arrange the classified characters as per a pre-setformat.
 13. The system as claimed in claim 12, wherein the pre-setformat is different from the sequence in which the classified charactersare presented in the portion.
 14. The system as claimed in claim 1,wherein the processor is configured to: determine whether re-orientationof the portion is required based on the determined orientation; reorientthe portion if re-orientation is required prior to extracting the text;and extract the text from the reoriented portion.
 15. A method forextracting text from images, the method comprising: receiving, by acomputing infrastructure, a digital copy of an image; identifying, bythe computing infrastructure, a portion of the image, wherein theportion comprises text to be extracted; determining, by the computinginfrastructure, orientation of the portion of the image; and extracting,by the computing infrastructure, text from the portion of the imageconsidering the orientation of the portion of the image.